{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **pytorch tensorflow keras**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> `标题 - 大框架`\n",
    "> `无序列表 - 小框架 `\n",
    "> `引用 - 对应具体案例`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 预测房价\n",
    "连续值、确定的属性 - 回归regression（监督学习）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 加载数据并分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = load_boston()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 了解数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR', 'data', 'feature_names', 'filename', 'target']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on Bunch in module sklearn.utils object:\n",
      "\n",
      "class Bunch(builtins.dict)\n",
      " |  Bunch(**kwargs)\n",
      " |  \n",
      " |  Container object for datasets\n",
      " |  \n",
      " |  Dictionary-like object that exposes its keys as attributes.\n",
      " |  \n",
      " |  >>> b = Bunch(a=1, b=2)\n",
      " |  >>> b['b']\n",
      " |  2\n",
      " |  >>> b.b\n",
      " |  2\n",
      " |  >>> b.a = 3\n",
      " |  >>> b['a']\n",
      " |  3\n",
      " |  >>> b.c = 6\n",
      " |  >>> b['c']\n",
      " |  6\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      Bunch\n",
      " |      builtins.dict\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __dir__(self)\n",
      " |      Default dir() implementation.\n",
      " |  \n",
      " |  __getattr__(self, key)\n",
      " |  \n",
      " |  __init__(self, **kwargs)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  __setattr__(self, key, value)\n",
      " |      Implement setattr(self, name, value).\n",
      " |  \n",
      " |  __setstate__(self, state)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from builtins.dict:\n",
      " |  \n",
      " |  __contains__(self, key, /)\n",
      " |      True if the dictionary has the specified key, else False.\n",
      " |  \n",
      " |  __delitem__(self, key, /)\n",
      " |      Delete self[key].\n",
      " |  \n",
      " |  __eq__(self, value, /)\n",
      " |      Return self==value.\n",
      " |  \n",
      " |  __ge__(self, value, /)\n",
      " |      Return self>=value.\n",
      " |  \n",
      " |  __getattribute__(self, name, /)\n",
      " |      Return getattr(self, name).\n",
      " |  \n",
      " |  __getitem__(...)\n",
      " |      x.__getitem__(y) <==> x[y]\n",
      " |  \n",
      " |  __gt__(self, value, /)\n",
      " |      Return self>value.\n",
      " |  \n",
      " |  __iter__(self, /)\n",
      " |      Implement iter(self).\n",
      " |  \n",
      " |  __le__(self, value, /)\n",
      " |      Return self<=value.\n",
      " |  \n",
      " |  __len__(self, /)\n",
      " |      Return len(self).\n",
      " |  \n",
      " |  __lt__(self, value, /)\n",
      " |      Return self<value.\n",
      " |  \n",
      " |  __ne__(self, value, /)\n",
      " |      Return self!=value.\n",
      " |  \n",
      " |  __repr__(self, /)\n",
      " |      Return repr(self).\n",
      " |  \n",
      " |  __setitem__(self, key, value, /)\n",
      " |      Set self[key] to value.\n",
      " |  \n",
      " |  __sizeof__(...)\n",
      " |      D.__sizeof__() -> size of D in memory, in bytes\n",
      " |  \n",
      " |  clear(...)\n",
      " |      D.clear() -> None.  Remove all items from D.\n",
      " |  \n",
      " |  copy(...)\n",
      " |      D.copy() -> a shallow copy of D\n",
      " |  \n",
      " |  get(self, key, default=None, /)\n",
      " |      Return the value for key if key is in the dictionary, else default.\n",
      " |  \n",
      " |  items(...)\n",
      " |      D.items() -> a set-like object providing a view on D's items\n",
      " |  \n",
      " |  keys(...)\n",
      " |      D.keys() -> a set-like object providing a view on D's keys\n",
      " |  \n",
      " |  pop(...)\n",
      " |      D.pop(k[,d]) -> v, remove specified key and return the corresponding value.\n",
      " |      If key is not found, d is returned if given, otherwise KeyError is raised\n",
      " |  \n",
      " |  popitem(...)\n",
      " |      D.popitem() -> (k, v), remove and return some (key, value) pair as a\n",
      " |      2-tuple; but raise KeyError if D is empty.\n",
      " |  \n",
      " |  setdefault(self, key, default=None, /)\n",
      " |      Insert key with a value of default if key is not in the dictionary.\n",
      " |      \n",
      " |      Return the value for key if key is in the dictionary, else default.\n",
      " |  \n",
      " |  update(...)\n",
      " |      D.update([E, ]**F) -> None.  Update D from dict/iterable E and F.\n",
      " |      If E is present and has a .keys() method, then does:  for k in E: D[k] = E[k]\n",
      " |      If E is present and lacks a .keys() method, then does:  for k, v in E: D[k] = v\n",
      " |      In either case, this is followed by: for k in F:  D[k] = F[k]\n",
      " |  \n",
      " |  values(...)\n",
      " |      D.values() -> an object providing a view on D's values\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Class methods inherited from builtins.dict:\n",
      " |  \n",
      " |  fromkeys(iterable, value=None, /) from builtins.type\n",
      " |      Create a new dictionary with keys from iterable and values set to value.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Static methods inherited from builtins.dict:\n",
      " |  \n",
      " |  __new__(*args, **kwargs) from builtins.type\n",
      " |      Create and return a new object.  See help(type) for accurate signature.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes inherited from builtins.dict:\n",
      " |  \n",
      " |  __hash__ = None\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n",
       "       'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['feature_names'] # 数据的表头缩写"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _boston_dataset:\n",
      "\n",
      "Boston house prices dataset\n",
      "---------------------------\n",
      "\n",
      "**Data Set Characteristics:**  \n",
      "\n",
      "    :Number of Instances: 506 \n",
      "\n",
      "    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\n",
      "\n",
      "    :Attribute Information (in order):\n",
      "        - CRIM     per capita crime rate by town\n",
      "        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n",
      "        - INDUS    proportion of non-retail business acres per town\n",
      "        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
      "        - NOX      nitric oxides concentration (parts per 10 million)\n",
      "        - RM       average number of rooms per dwelling\n",
      "        - AGE      proportion of owner-occupied units built prior to 1940\n",
      "        - DIS      weighted distances to five Boston employment centres\n",
      "        - RAD      index of accessibility to radial highways\n",
      "        - TAX      full-value property-tax rate per $10,000\n",
      "        - PTRATIO  pupil-teacher ratio by town\n",
      "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
      "        - LSTAT    % lower status of the population\n",
      "        - MEDV     Median value of owner-occupied homes in $1000's\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Creator: Harrison, D. and Rubinfeld, D.L.\n",
      "\n",
      "This is a copy of UCI ML housing dataset.\n",
      "https://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n",
      "\n",
      "\n",
      "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
      "\n",
      "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
      "prices and the demand for clean air', J. Environ. Economics & Management,\n",
      "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
      "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
      "pages 244-261 of the latter.\n",
      "\n",
      "The Boston house-price data has been used in many machine learning papers that address regression\n",
      "problems.   \n",
      "     \n",
      ".. topic:: References\n",
      "\n",
      "   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
      "   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(dataset['DESCR']) # 全表头含义"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 定义问题\n",
    ">面积推测售价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe = pd.DataFrame(dataset['data'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "506"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>0.06263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.593</td>\n",
       "      <td>69.1</td>\n",
       "      <td>2.4786</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>391.99</td>\n",
       "      <td>9.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>0.04527</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.120</td>\n",
       "      <td>76.7</td>\n",
       "      <td>2.2875</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>0.06076</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.976</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2.1675</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>0.10959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.794</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2.3889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>393.45</td>\n",
       "      <td>6.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>0.04741</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.030</td>\n",
       "      <td>80.8</td>\n",
       "      <td>2.5050</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>7.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0     1      2    3      4      5     6       7    8      9    10  \\\n",
       "0    0.00632  18.0   2.31  0.0  0.538  6.575  65.2  4.0900  1.0  296.0  15.3   \n",
       "1    0.02731   0.0   7.07  0.0  0.469  6.421  78.9  4.9671  2.0  242.0  17.8   \n",
       "2    0.02729   0.0   7.07  0.0  0.469  7.185  61.1  4.9671  2.0  242.0  17.8   \n",
       "3    0.03237   0.0   2.18  0.0  0.458  6.998  45.8  6.0622  3.0  222.0  18.7   \n",
       "4    0.06905   0.0   2.18  0.0  0.458  7.147  54.2  6.0622  3.0  222.0  18.7   \n",
       "..       ...   ...    ...  ...    ...    ...   ...     ...  ...    ...   ...   \n",
       "501  0.06263   0.0  11.93  0.0  0.573  6.593  69.1  2.4786  1.0  273.0  21.0   \n",
       "502  0.04527   0.0  11.93  0.0  0.573  6.120  76.7  2.2875  1.0  273.0  21.0   \n",
       "503  0.06076   0.0  11.93  0.0  0.573  6.976  91.0  2.1675  1.0  273.0  21.0   \n",
       "504  0.10959   0.0  11.93  0.0  0.573  6.794  89.3  2.3889  1.0  273.0  21.0   \n",
       "505  0.04741   0.0  11.93  0.0  0.573  6.030  80.8  2.5050  1.0  273.0  21.0   \n",
       "\n",
       "         11    12  \n",
       "0    396.90  4.98  \n",
       "1    396.90  9.14  \n",
       "2    392.83  4.03  \n",
       "3    394.63  2.94  \n",
       "4    396.90  5.33  \n",
       "..      ...   ...  \n",
       "501  391.99  9.67  \n",
       "502  396.90  9.08  \n",
       "503  396.90  5.64  \n",
       "504  393.45  6.48  \n",
       "505  396.90  7.88  \n",
       "\n",
       "[506 rows x 13 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe # 显示接收到的全表数据（表头不明晰）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe.columns = dataset['feature_names'] # 将表头缩写赋给数据表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
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       "4    0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "..       ...   ...    ...   ...    ...    ...   ...     ...  ...    ...   \n",
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       "\n",
       "     PTRATIO       B  LSTAT  \n",
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       "1       17.8  396.90   9.14  \n",
       "2       17.8  392.83   4.03  \n",
       "3       18.7  394.63   2.94  \n",
       "4       18.7  396.90   5.33  \n",
       "..       ...     ...    ...  \n",
       "501     21.0  391.99   9.67  \n",
       "502     21.0  396.90   9.08  \n",
       "503     21.0  396.90   5.64  \n",
       "504     21.0  393.45   6.48  \n",
       "505     21.0  396.90   7.88  \n",
       "\n",
       "[506 rows x 13 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 添加价格列\n",
    "dataframe['price'] = dataset['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>506 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0    0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1    0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2    0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3    0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4    0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "..       ...   ...    ...   ...    ...    ...   ...     ...  ...    ...   \n",
       "501  0.06263   0.0  11.93   0.0  0.573  6.593  69.1  2.4786  1.0  273.0   \n",
       "502  0.04527   0.0  11.93   0.0  0.573  6.120  76.7  2.2875  1.0  273.0   \n",
       "503  0.06076   0.0  11.93   0.0  0.573  6.976  91.0  2.1675  1.0  273.0   \n",
       "504  0.10959   0.0  11.93   0.0  0.573  6.794  89.3  2.3889  1.0  273.0   \n",
       "505  0.04741   0.0  11.93   0.0  0.573  6.030  80.8  2.5050  1.0  273.0   \n",
       "\n",
       "     PTRATIO       B  LSTAT  price  \n",
       "0       15.3  396.90   4.98   24.0  \n",
       "1       17.8  396.90   9.14   21.6  \n",
       "2       17.8  392.83   4.03   34.7  \n",
       "3       18.7  394.63   2.94   33.4  \n",
       "4       18.7  396.90   5.33   36.2  \n",
       "..       ...     ...    ...    ...  \n",
       "501     21.0  391.99   9.67   22.4  \n",
       "502     21.0  396.90   9.08   20.6  \n",
       "503     21.0  396.90   5.64   23.9  \n",
       "504     21.0  393.45   6.48   22.0  \n",
       "505     21.0  396.90   7.88   11.9  \n",
       "\n",
       "[506 rows x 14 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 什么因素对房价影响最大？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CRIM</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.200469</td>\n",
       "      <td>0.406583</td>\n",
       "      <td>-0.055892</td>\n",
       "      <td>0.420972</td>\n",
       "      <td>-0.219247</td>\n",
       "      <td>0.352734</td>\n",
       "      <td>-0.379670</td>\n",
       "      <td>0.625505</td>\n",
       "      <td>0.582764</td>\n",
       "      <td>0.289946</td>\n",
       "      <td>-0.385064</td>\n",
       "      <td>0.455621</td>\n",
       "      <td>-0.388305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZN</th>\n",
       "      <td>-0.200469</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.533828</td>\n",
       "      <td>-0.042697</td>\n",
       "      <td>-0.516604</td>\n",
       "      <td>0.311991</td>\n",
       "      <td>-0.569537</td>\n",
       "      <td>0.664408</td>\n",
       "      <td>-0.311948</td>\n",
       "      <td>-0.314563</td>\n",
       "      <td>-0.391679</td>\n",
       "      <td>0.175520</td>\n",
       "      <td>-0.412995</td>\n",
       "      <td>0.360445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDUS</th>\n",
       "      <td>0.406583</td>\n",
       "      <td>-0.533828</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.062938</td>\n",
       "      <td>0.763651</td>\n",
       "      <td>-0.391676</td>\n",
       "      <td>0.644779</td>\n",
       "      <td>-0.708027</td>\n",
       "      <td>0.595129</td>\n",
       "      <td>0.720760</td>\n",
       "      <td>0.383248</td>\n",
       "      <td>-0.356977</td>\n",
       "      <td>0.603800</td>\n",
       "      <td>-0.483725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHAS</th>\n",
       "      <td>-0.055892</td>\n",
       "      <td>-0.042697</td>\n",
       "      <td>0.062938</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.091203</td>\n",
       "      <td>0.091251</td>\n",
       "      <td>0.086518</td>\n",
       "      <td>-0.099176</td>\n",
       "      <td>-0.007368</td>\n",
       "      <td>-0.035587</td>\n",
       "      <td>-0.121515</td>\n",
       "      <td>0.048788</td>\n",
       "      <td>-0.053929</td>\n",
       "      <td>0.175260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOX</th>\n",
       "      <td>0.420972</td>\n",
       "      <td>-0.516604</td>\n",
       "      <td>0.763651</td>\n",
       "      <td>0.091203</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.302188</td>\n",
       "      <td>0.731470</td>\n",
       "      <td>-0.769230</td>\n",
       "      <td>0.611441</td>\n",
       "      <td>0.668023</td>\n",
       "      <td>0.188933</td>\n",
       "      <td>-0.380051</td>\n",
       "      <td>0.590879</td>\n",
       "      <td>-0.427321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RM</th>\n",
       "      <td>-0.219247</td>\n",
       "      <td>0.311991</td>\n",
       "      <td>-0.391676</td>\n",
       "      <td>0.091251</td>\n",
       "      <td>-0.302188</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.240265</td>\n",
       "      <td>0.205246</td>\n",
       "      <td>-0.209847</td>\n",
       "      <td>-0.292048</td>\n",
       "      <td>-0.355501</td>\n",
       "      <td>0.128069</td>\n",
       "      <td>-0.613808</td>\n",
       "      <td>0.695360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE</th>\n",
       "      <td>0.352734</td>\n",
       "      <td>-0.569537</td>\n",
       "      <td>0.644779</td>\n",
       "      <td>0.086518</td>\n",
       "      <td>0.731470</td>\n",
       "      <td>-0.240265</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.747881</td>\n",
       "      <td>0.456022</td>\n",
       "      <td>0.506456</td>\n",
       "      <td>0.261515</td>\n",
       "      <td>-0.273534</td>\n",
       "      <td>0.602339</td>\n",
       "      <td>-0.376955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIS</th>\n",
       "      <td>-0.379670</td>\n",
       "      <td>0.664408</td>\n",
       "      <td>-0.708027</td>\n",
       "      <td>-0.099176</td>\n",
       "      <td>-0.769230</td>\n",
       "      <td>0.205246</td>\n",
       "      <td>-0.747881</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.494588</td>\n",
       "      <td>-0.534432</td>\n",
       "      <td>-0.232471</td>\n",
       "      <td>0.291512</td>\n",
       "      <td>-0.496996</td>\n",
       "      <td>0.249929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD</th>\n",
       "      <td>0.625505</td>\n",
       "      <td>-0.311948</td>\n",
       "      <td>0.595129</td>\n",
       "      <td>-0.007368</td>\n",
       "      <td>0.611441</td>\n",
       "      <td>-0.209847</td>\n",
       "      <td>0.456022</td>\n",
       "      <td>-0.494588</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.910228</td>\n",
       "      <td>0.464741</td>\n",
       "      <td>-0.444413</td>\n",
       "      <td>0.488676</td>\n",
       "      <td>-0.381626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAX</th>\n",
       "      <td>0.582764</td>\n",
       "      <td>-0.314563</td>\n",
       "      <td>0.720760</td>\n",
       "      <td>-0.035587</td>\n",
       "      <td>0.668023</td>\n",
       "      <td>-0.292048</td>\n",
       "      <td>0.506456</td>\n",
       "      <td>-0.534432</td>\n",
       "      <td>0.910228</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.460853</td>\n",
       "      <td>-0.441808</td>\n",
       "      <td>0.543993</td>\n",
       "      <td>-0.468536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTRATIO</th>\n",
       "      <td>0.289946</td>\n",
       "      <td>-0.391679</td>\n",
       "      <td>0.383248</td>\n",
       "      <td>-0.121515</td>\n",
       "      <td>0.188933</td>\n",
       "      <td>-0.355501</td>\n",
       "      <td>0.261515</td>\n",
       "      <td>-0.232471</td>\n",
       "      <td>0.464741</td>\n",
       "      <td>0.460853</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.177383</td>\n",
       "      <td>0.374044</td>\n",
       "      <td>-0.507787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.385064</td>\n",
       "      <td>0.175520</td>\n",
       "      <td>-0.356977</td>\n",
       "      <td>0.048788</td>\n",
       "      <td>-0.380051</td>\n",
       "      <td>0.128069</td>\n",
       "      <td>-0.273534</td>\n",
       "      <td>0.291512</td>\n",
       "      <td>-0.444413</td>\n",
       "      <td>-0.441808</td>\n",
       "      <td>-0.177383</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.366087</td>\n",
       "      <td>0.333461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LSTAT</th>\n",
       "      <td>0.455621</td>\n",
       "      <td>-0.412995</td>\n",
       "      <td>0.603800</td>\n",
       "      <td>-0.053929</td>\n",
       "      <td>0.590879</td>\n",
       "      <td>-0.613808</td>\n",
       "      <td>0.602339</td>\n",
       "      <td>-0.496996</td>\n",
       "      <td>0.488676</td>\n",
       "      <td>0.543993</td>\n",
       "      <td>0.374044</td>\n",
       "      <td>-0.366087</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.737663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>-0.388305</td>\n",
       "      <td>0.360445</td>\n",
       "      <td>-0.483725</td>\n",
       "      <td>0.175260</td>\n",
       "      <td>-0.427321</td>\n",
       "      <td>0.695360</td>\n",
       "      <td>-0.376955</td>\n",
       "      <td>0.249929</td>\n",
       "      <td>-0.381626</td>\n",
       "      <td>-0.468536</td>\n",
       "      <td>-0.507787</td>\n",
       "      <td>0.333461</td>\n",
       "      <td>-0.737663</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM        ZN     INDUS      CHAS       NOX        RM       AGE  \\\n",
       "CRIM     1.000000 -0.200469  0.406583 -0.055892  0.420972 -0.219247  0.352734   \n",
       "ZN      -0.200469  1.000000 -0.533828 -0.042697 -0.516604  0.311991 -0.569537   \n",
       "INDUS    0.406583 -0.533828  1.000000  0.062938  0.763651 -0.391676  0.644779   \n",
       "CHAS    -0.055892 -0.042697  0.062938  1.000000  0.091203  0.091251  0.086518   \n",
       "NOX      0.420972 -0.516604  0.763651  0.091203  1.000000 -0.302188  0.731470   \n",
       "RM      -0.219247  0.311991 -0.391676  0.091251 -0.302188  1.000000 -0.240265   \n",
       "AGE      0.352734 -0.569537  0.644779  0.086518  0.731470 -0.240265  1.000000   \n",
       "DIS     -0.379670  0.664408 -0.708027 -0.099176 -0.769230  0.205246 -0.747881   \n",
       "RAD      0.625505 -0.311948  0.595129 -0.007368  0.611441 -0.209847  0.456022   \n",
       "TAX      0.582764 -0.314563  0.720760 -0.035587  0.668023 -0.292048  0.506456   \n",
       "PTRATIO  0.289946 -0.391679  0.383248 -0.121515  0.188933 -0.355501  0.261515   \n",
       "B       -0.385064  0.175520 -0.356977  0.048788 -0.380051  0.128069 -0.273534   \n",
       "LSTAT    0.455621 -0.412995  0.603800 -0.053929  0.590879 -0.613808  0.602339   \n",
       "price   -0.388305  0.360445 -0.483725  0.175260 -0.427321  0.695360 -0.376955   \n",
       "\n",
       "              DIS       RAD       TAX   PTRATIO         B     LSTAT     price  \n",
       "CRIM    -0.379670  0.625505  0.582764  0.289946 -0.385064  0.455621 -0.388305  \n",
       "ZN       0.664408 -0.311948 -0.314563 -0.391679  0.175520 -0.412995  0.360445  \n",
       "INDUS   -0.708027  0.595129  0.720760  0.383248 -0.356977  0.603800 -0.483725  \n",
       "CHAS    -0.099176 -0.007368 -0.035587 -0.121515  0.048788 -0.053929  0.175260  \n",
       "NOX     -0.769230  0.611441  0.668023  0.188933 -0.380051  0.590879 -0.427321  \n",
       "RM       0.205246 -0.209847 -0.292048 -0.355501  0.128069 -0.613808  0.695360  \n",
       "AGE     -0.747881  0.456022  0.506456  0.261515 -0.273534  0.602339 -0.376955  \n",
       "DIS      1.000000 -0.494588 -0.534432 -0.232471  0.291512 -0.496996  0.249929  \n",
       "RAD     -0.494588  1.000000  0.910228  0.464741 -0.444413  0.488676 -0.381626  \n",
       "TAX     -0.534432  0.910228  1.000000  0.460853 -0.441808  0.543993 -0.468536  \n",
       "PTRATIO -0.232471  0.464741  0.460853  1.000000 -0.177383  0.374044 -0.507787  \n",
       "B        0.291512 -0.444413 -0.441808 -0.177383  1.000000 -0.366087  0.333461  \n",
       "LSTAT   -0.496996  0.488676  0.543993  0.374044 -0.366087  1.000000 -0.737663  \n",
       "price    0.249929 -0.381626 -0.468536 -0.507787  0.333461 -0.737663  1.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.corr() # 相关性表"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 对相关性的分析\n",
    ">>price \t-0.388305 \t0.360445 \t-0.483725 \t0.175260 \t-0.427321 \t0.695360 \t-0.376955 \n",
    "0.249929 \t-0.381626 \t-0.468536 \t-0.507787 \t0.333461 \t-0.737663 \t1.000000\n",
    "- LSTAT 影响因子最负相关 达-0.73\n",
    "- 低收入人群比例 % lower status of the population\n",
    "- RM 影响因子最正相关\n",
    "- 房屋平均房间数量 average number of rooms per dwelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['FacetGrid',\n",
       " 'JointGrid',\n",
       " 'PairGrid',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " '_orig_rc_params',\n",
       " 'algorithms',\n",
       " 'axes_style',\n",
       " 'axisgrid',\n",
       " 'barplot',\n",
       " 'blend_palette',\n",
       " 'boxenplot',\n",
       " 'boxplot',\n",
       " 'categorical',\n",
       " 'catplot',\n",
       " 'choose_colorbrewer_palette',\n",
       " 'choose_cubehelix_palette',\n",
       " 'choose_dark_palette',\n",
       " 'choose_diverging_palette',\n",
       " 'choose_light_palette',\n",
       " 'clustermap',\n",
       " 'cm',\n",
       " 'color_palette',\n",
       " 'colors',\n",
       " 'countplot',\n",
       " 'crayon_palette',\n",
       " 'crayons',\n",
       " 'cubehelix_palette',\n",
       " 'dark_palette',\n",
       " 'desaturate',\n",
       " 'despine',\n",
       " 'distplot',\n",
       " 'distributions',\n",
       " 'diverging_palette',\n",
       " 'dogplot',\n",
       " 'external',\n",
       " 'factorplot',\n",
       " 'get_dataset_names',\n",
       " 'heatmap',\n",
       " 'hls_palette',\n",
       " 'husl_palette',\n",
       " 'jointplot',\n",
       " 'kdeplot',\n",
       " 'light_palette',\n",
       " 'lineplot',\n",
       " 'lmplot',\n",
       " 'load_dataset',\n",
       " 'lvplot',\n",
       " 'matrix',\n",
       " 'miscplot',\n",
       " 'mpl',\n",
       " 'mpl_palette',\n",
       " 'pairplot',\n",
       " 'palettes',\n",
       " 'palplot',\n",
       " 'plotting_context',\n",
       " 'pointplot',\n",
       " 'rcmod',\n",
       " 'regplot',\n",
       " 'regression',\n",
       " 'relational',\n",
       " 'relplot',\n",
       " 'reset_defaults',\n",
       " 'reset_orig',\n",
       " 'residplot',\n",
       " 'rugplot',\n",
       " 'saturate',\n",
       " 'scatterplot',\n",
       " 'set',\n",
       " 'set_color_codes',\n",
       " 'set_context',\n",
       " 'set_hls_values',\n",
       " 'set_palette',\n",
       " 'set_style',\n",
       " 'stripplot',\n",
       " 'swarmplot',\n",
       " 'utils',\n",
       " 'violinplot',\n",
       " 'widgets',\n",
       " 'xkcd_palette',\n",
       " 'xkcd_rgb']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(sns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2faa2e0fd0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 相关性-热力图\n",
    "sns.heatmap(dataframe.corr(),annot=True, fmt='.1f')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 影响因子回归"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 简化：如何根据 房屋所含房间数量 预测 房价？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      6.575\n",
       "1      6.421\n",
       "2      7.185\n",
       "3      6.998\n",
       "4      7.147\n",
       "       ...  \n",
       "501    6.593\n",
       "502    6.120\n",
       "503    6.976\n",
       "504    6.794\n",
       "505    6.030\n",
       "Name: RM, Length: 506, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 画出折线图\n",
    "dataframe['RM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_rm = dataframe['RM']\n",
    "Y = dataframe['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "rm_to_price = {r: y for r, y in zip(X_rm, Y) }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{6.575: 24.0,\n",
       " 6.421: 21.6,\n",
       " 7.185: 34.9,\n",
       " 6.998: 33.4,\n",
       " 7.147: 36.2,\n",
       " 6.43: 28.7,\n",
       " 6.012: 22.9,\n",
       " 6.172: 27.1,\n",
       " 5.631: 16.5,\n",
       " 6.004: 20.3,\n",
       " 6.377: 15.0,\n",
       " 6.009: 21.7,\n",
       " 5.889: 21.7,\n",
       " 5.949: 20.4,\n",
       " 6.096: 13.5,\n",
       " 5.834: 19.9,\n",
       " 5.935: 8.4,\n",
       " 5.99: 17.5,\n",
       " 5.456: 20.2,\n",
       " 5.727: 18.2,\n",
       " 5.57: 13.6,\n",
       " 5.965: 19.6,\n",
       " 6.142: 15.2,\n",
       " 5.813: 16.6,\n",
       " 5.924: 15.6,\n",
       " 5.599: 13.9,\n",
       " 6.047: 14.8,\n",
       " 6.495: 26.4,\n",
       " 6.674: 21.0,\n",
       " 5.713: 20.1,\n",
       " 6.072: 14.5,\n",
       " 5.95: 13.2,\n",
       " 5.701: 13.1,\n",
       " 5.933: 18.9,\n",
       " 5.841: 20.0,\n",
       " 5.85: 21.0,\n",
       " 5.966: 16.0,\n",
       " 6.595: 30.8,\n",
       " 7.024: 34.9,\n",
       " 6.77: 26.6,\n",
       " 6.169: 25.3,\n",
       " 6.211: 25.0,\n",
       " 6.069: 21.2,\n",
       " 5.682: 19.3,\n",
       " 5.786: 20.0,\n",
       " 6.03: 11.9,\n",
       " 5.399: 14.4,\n",
       " 5.602: 19.4,\n",
       " 5.963: 19.7,\n",
       " 6.115: 20.5,\n",
       " 6.511: 25.0,\n",
       " 5.998: 23.4,\n",
       " 5.888: 23.3,\n",
       " 7.249: 35.4,\n",
       " 6.383: 24.7,\n",
       " 6.816: 31.6,\n",
       " 6.145: 23.3,\n",
       " 5.927: 19.6,\n",
       " 5.741: 18.7,\n",
       " 6.456: 22.2,\n",
       " 6.762: 25.0,\n",
       " 7.104: 33.0,\n",
       " 6.29: 23.5,\n",
       " 5.787: 19.4,\n",
       " 5.878: 22.0,\n",
       " 5.594: 17.4,\n",
       " 5.885: 20.9,\n",
       " 6.417: 13.0,\n",
       " 5.961: 20.5,\n",
       " 6.065: 22.8,\n",
       " 6.245: 23.4,\n",
       " 6.273: 24.1,\n",
       " 6.286: 21.4,\n",
       " 6.279: 20.0,\n",
       " 6.14: 20.8,\n",
       " 6.232: 21.2,\n",
       " 5.874: 20.3,\n",
       " 6.727: 27.5,\n",
       " 6.619: 23.9,\n",
       " 6.302: 24.8,\n",
       " 6.167: 19.9,\n",
       " 6.389: 23.9,\n",
       " 6.63: 27.9,\n",
       " 6.015: 22.5,\n",
       " 6.121: 22.2,\n",
       " 7.007: 23.6,\n",
       " 7.079: 28.7,\n",
       " 6.405: 12.5,\n",
       " 6.442: 22.9,\n",
       " 6.249: 20.6,\n",
       " 6.625: 28.4,\n",
       " 6.163: 21.4,\n",
       " 8.069: 38.7,\n",
       " 7.82: 45.4,\n",
       " 7.416: 33.2,\n",
       " 6.781: 26.5,\n",
       " 6.137: 19.3,\n",
       " 5.851: 19.5,\n",
       " 5.836: 19.5,\n",
       " 6.127: 22.7,\n",
       " 6.474: 19.8,\n",
       " 6.229: 21.4,\n",
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       " 5.362: 20.8,\n",
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       " 6.794: 22.0,\n",
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       " 6.223: 10.2,\n",
       " 6.545: 10.9,\n",
       " 5.536: 11.3,\n",
       " 5.52: 12.3,\n",
       " 4.368: 8.8,\n",
       " 5.277: 7.2,\n",
       " 4.652: 10.5,\n",
       " 5.0: 7.4,\n",
       " 4.88: 10.2,\n",
       " 5.39: 19.7,\n",
       " 6.051: 23.2,\n",
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       " 5.987: 5.6,\n",
       " 6.343: 7.2,\n",
       " 6.404: 12.1,\n",
       " 5.349: 8.3,\n",
       " 5.531: 8.5,\n",
       " 5.683: 5.0,\n",
       " 5.608: 27.9,\n",
       " 5.617: 17.2,\n",
       " 6.852: 27.5,\n",
       " 6.657: 17.2,\n",
       " 4.628: 17.9,\n",
       " 5.155: 16.3,\n",
       " 4.519: 7.0,\n",
       " 6.434: 7.2,\n",
       " 5.304: 12.0,\n",
       " 5.957: 8.8,\n",
       " 6.824: 8.4,\n",
       " 6.411: 16.7,\n",
       " 6.006: 14.2,\n",
       " 5.648: 20.8,\n",
       " 6.103: 13.4,\n",
       " 5.565: 11.7,\n",
       " 5.896: 8.3,\n",
       " 5.837: 10.2,\n",
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       " 6.425: 16.1,\n",
       " 6.436: 14.3,\n",
       " 6.208: 11.7,\n",
       " 6.629: 13.4,\n",
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       " 6.459: 11.8,\n",
       " 6.341: 14.9,\n",
       " 6.185: 14.6,\n",
       " 6.749: 13.4,\n",
       " 6.655: 15.2,\n",
       " 6.297: 16.1,\n",
       " 7.393: 17.8,\n",
       " 6.525: 14.1,\n",
       " 5.976: 12.7,\n",
       " 6.301: 14.9,\n",
       " 6.081: 20.0,\n",
       " 6.701: 16.4,\n",
       " 6.317: 19.5,\n",
       " 6.513: 20.2,\n",
       " 5.759: 19.9,\n",
       " 5.952: 19.0,\n",
       " 6.003: 19.1,\n",
       " 5.926: 24.5,\n",
       " 6.437: 23.2,\n",
       " 5.427: 13.8,\n",
       " 6.484: 16.7,\n",
       " 6.242: 23.0,\n",
       " 6.75: 23.7,\n",
       " 7.061: 25.0,\n",
       " 5.762: 21.8,\n",
       " 5.871: 20.6,\n",
       " 6.114: 19.1,\n",
       " 5.905: 20.6,\n",
       " 5.454: 15.2,\n",
       " 5.414: 7.0,\n",
       " 5.093: 8.1,\n",
       " 5.983: 20.1,\n",
       " 5.707: 21.8,\n",
       " 5.67: 23.1,\n",
       " 5.794: 18.3,\n",
       " 6.019: 21.2,\n",
       " 5.569: 17.5,\n",
       " 6.027: 16.8,\n",
       " 6.593: 22.4,\n",
       " 6.12: 20.6,\n",
       " 6.976: 23.9}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rm_to_price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K近邻算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# KNN-means k近邻\n",
    "# 解决 房间数量为小数(不合理) 问题\n",
    "import numpy as np # 导入numpy包\n",
    "def knn(query_x, history, top_n=3):\n",
    "    # sorted(排序对象, 排序规则)\n",
    "    sorted_notes = sorted(history.items(), key=lambda x_y: (x_y[0] - query_x) ** 2) # 按照 与指定的房间数量接近程度 排序\n",
    "    # [:取值个数]\n",
    "    similar_notes = sorted_notes[:top_n]\n",
    "    # 取数据对的属性之一封装为list []\n",
    "    similar_ys = [y for _, y in similar_notes]\n",
    "    print(similar_ys)\n",
    " \n",
    "    return np.mean(similar_ys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7.4, 15.3, 14.4]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "12.366666666666667"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn(5, rm_to_price)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 机器学习  \n",
    "KNN比较低效  \n",
    "> 寻找函数关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f2faa425150>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制数据散点图\n",
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(X_rm, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 找到拟合函数\n",
    "# 使得 预测值与实际值的平方差均值 最小"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ Loss(k, b) = \\frac{1}{n} \\sum_{i \\in N} (\\hat{y_i} - y_i) ^ 2 $$\n",
    "$$ Loss(k, b) = \\frac{1}{n} \\sum_{i \\in N} ((k * rm_i + b) - y_i) ^ 2 $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 判断标准:损失函数\n",
    "> MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MSE\n",
    "def loss(y1, y2):\n",
    "    return np.mean((y1 - y2) ** 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 确定 K & B  \n",
    "> 随机数法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "在第1步，我们获得了函数 f(rm) = 87 * rm + -96, 此时loss是: 186414.57819988736\n",
      "在第2步，我们获得了函数 f(rm) = -65 * rm + 87, 此时loss是: 121108.47207821143\n",
      "在第5步，我们获得了函数 f(rm) = -1 * rm + 48, 此时loss是: 461.85196333399233\n",
      "在第17步，我们获得了函数 f(rm) = 8 * rm + -34, 此时loss是: 83.33317432411066\n",
      "在第286步，我们获得了函数 f(rm) = 11 * rm + -44, 此时loss是: 52.12573258300395\n",
      "在第947步，我们获得了函数 f(rm) = 6 * rm + -16, 此时loss是: 49.02242231620553\n",
      "在第1195步，我们获得了函数 f(rm) = 11 * rm + -47, 此时loss是: 45.53670096245059\n"
     ]
    }
   ],
   "source": [
    "import random \n",
    "min_loss = float('inf') # 记录最小损失值\n",
    "best_k, bes_b = None, None # 记录最优k,b\n",
    "\n",
    "for step in range(8000): # 随机试验次数\n",
    "    min_v, max_v = -100, 100 # k,b的试验范围\n",
    "    k, b = random.randrange(min_v, max_v), random.randrange(min_v, max_v) # 取随机值\n",
    "    Y_predict = [k * rm_i  + b for rm_i in X_rm] # 随机预测\n",
    "    current_loss = loss(Y_predict, Y) # 计算该次损失值\n",
    "    \n",
    "    if current_loss < min_loss:\n",
    "        min_loss = current_loss\n",
    "        best_k, best_b = k, b\n",
    "        print('在第{}步，我们获得了函数 f(rm) = {} * rm + {}, 此时loss是: {}'.format(step+1, k, b, current_loss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f2fa434abd0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_rm, Y)\n",
    "plt.scatter(X_rm, [best_k * rm + best_b for rm in X_rm])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Q:越往后优化效率越低&效果越差\n",
    "## 蒙特卡洛模拟\n",
    "通过求导判断当前 点y=kx+b左右 loss升降趋势, 降低loss(导正:降y,导负:增y), 并调整k,b\n",
    "\n",
    "几乎每次都比较前次趋于最优，大幅度减少低效率随机试错"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ Loss(k, b) = \\frac{1}{n} \\sum_{i \\in N} ((k * rm_i + b) - y_i) ^ 2 $$\n",
    "`梯度`\n",
    "\n",
    "$$ \\frac{\\partial{loss(k, b)}}{\\partial{k}} = \\frac{2}{n}\\sum_{i \\in N}(k * rm_i + b - y_i) * rm_i $$ \n",
    "\n",
    "$$ \\frac{\\partial{loss(k, b)}}{\\partial{b}} = \\frac{2}{n}\\sum_{i \\in N}(k * rm_i + b - y_i)$$\n",
    "`梯度下降`\n",
    "\n",
    "$$ k_{n+1} = k_n + -1 * \\frac{\\partial{loss(k, b)}}{\\partial{k_n}} $$\n",
    "\n",
    "$$ b_{n+1} = b_n + -1 * \\frac{\\partial{loss(k, b)}}{\\partial{b_n}} $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 深度学习的核心：通过梯度下降的方法，获得一组参数，使得loss最小，达到有限最优\n",
    "> 参数调优：  1-learning_rate  2-range(5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 对k求偏导\n",
    "def partial_k(k, b, x, y):\n",
    "    return 2 * np.mean((k * x + b - y) * x)\n",
    "# 对b求偏导\n",
    "def partial_b(k, b, x, y):\n",
    "    return 2 * np.mean(k * x + b - y)\n",
    "\n",
    "k, b = random.random(), random.random() # 无需限制参数取值范围（每次自由梯度下降都会更接近最优值）\n",
    "min_loss = float('inf')\n",
    "best_k, bes_b = None, None\n",
    "learning_rate = 1e-2 # 存在一个较优值，偏小偏大都会使loss结果不理想\n",
    "\n",
    "for step in range(5000): # 拟合次数越多，loss优化越多\n",
    "    # 梯度下降\n",
    "    k = k + (-1 * partial_k(k, b, X_rm, Y) * learning_rate)\n",
    "    b = b + (-1 * partial_b(k, b, X_rm, Y) * learning_rate)\n",
    "    y_hats = k * X_rm + b\n",
    "    current_loss = loss(y_hats, Y)\n",
    "    \n",
    "    if current_loss < min_loss:\n",
    "        min_loss = current_loss\n",
    "        best_k, best_b = k, b\n",
    "        # print('在第{}步，我们获得了函数 f(rm) = {} * rm + {}, 此时loss是: {}'.format(step+1, k, b, current_loss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44.936119345777655"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f2f9e06c790>]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_rm, Y)\n",
    "plt.scatter(X_rm, [best_k * rm + best_b for rm in X_rm])\n",
    "plt.plot(X_rm, best_k * X_rm + best_b)"
   ]
  },
  {
   "attachments": {
    "%E5%9B%BE%E7%89%87.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![%E5%9B%BE%E7%89%87.png](attachment:%E5%9B%BE%E7%89%87.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
